A Python code was created to make this plot.
The Python code simulates the natural decomposition of organic material under anaerobic conditions, mimicking processes that occur in nature. Specifically, it models how a given mixture of substrates, such as monosaccharides, amino acids (AA), and carboxylic acids, is broken down by various groups of bacteria into different by-products, ultimately producing biogas (a mixture of methane and carbon dioxide).
Substrate Decomposition:
The initial mixture of organic substrates serves as the feedstock.
Specialized bacteria decompose these substrates in a stepwise manner, following the pathways described by the Anaerobic Digestion Model No. 1 (ADM1).
Bacterial Activity:
Different bacterial groups perform specific roles:
Hydrolytic bacteria break down complex organic molecules into simpler compounds.
Acidogenic bacteria convert these compounds into volatile fatty acids and alcohols.
Acetogenic bacteria oxidize volatile fatty acids into acetic acid, hydrogen, and carbon dioxide.
Methanogenic archaea convert acetic acid and hydrogen into methane and carbon dioxide.
By-Products:
The breakdown of substrates produces intermediate compounds (e.g., volatile fatty acids, hydrogen) and final products (e.g., methane, carbon dioxide).
The gas phase reflects the production and release of biogas.
Model Representation:
The model uses a system of ordinary differential equations (ODEs) to represent these biological and chemical processes mathematically.
Reaction rates, inhibition effects, and environmental factors are parameterized to reflect real-world anaerobic conditions.
This model reproduces the behavior of anaerobic digestion systems to study:
How substrates are decomposed over time.
The production rates of by-products like biogas.
The effects of substrate composition and environmental conditions on system performance.
The simulation helps predict how organic material degrades under anaerobic conditions, providing insights into optimizing biogas production systems.
It enables researchers to test various conditions, substrate compositions, or parameter settings without conducting costly experiments, thus aiding in system design and troubleshooting.
Here the Equations: